Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


        Choose Plots

        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        Save Settings

        You can save the toolbox settings for this report to the browser.


        Load Settings

        Choose a saved report profile from the dropdown box below:

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.21

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/ampliseq analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-09-04, 12:44 EDT based on data in: /work/gmgi/Fisheries/eDNA/NY/scripts/work/a0/07f70607215e3dff1eaadc922b9d10


        General Statistics

        Showing 45/45 rows and 4/7 columns.
        Sample Name% BP Trimmed% Dups% GCM Seqs
        NTC
        57.6%
        NTC_1
        62.1%
        66%
        0.0M
        NTC_2
        87.7%
        64%
        0.0M
        PAC
        10.7%
        PAC_1
        99.2%
        50%
        0.2M
        PAC_2
        98.7%
        50%
        0.2M
        Site1_1
        14.9%
        Site1_1_1
        95.4%
        49%
        0.1M
        Site1_1_2
        97.0%
        47%
        0.1M
        Site1_2
        14.8%
        Site1_2_1
        97.9%
        48%
        0.4M
        Site1_2_2
        97.2%
        47%
        0.4M
        Site1_3
        14.5%
        Site1_3_1
        97.7%
        48%
        0.2M
        Site1_3_2
        96.9%
        46%
        0.2M
        Site2_1
        13.0%
        Site2_1_1
        95.2%
        48%
        0.1M
        Site2_1_2
        97.4%
        46%
        0.1M
        Site2_2
        13.1%
        Site2_2_1
        95.4%
        48%
        0.1M
        Site2_2_2
        97.1%
        46%
        0.1M
        Site2_3
        14.1%
        Site2_3_1
        97.5%
        48%
        0.3M
        Site2_3_2
        96.9%
        47%
        0.3M
        Site3_1
        9.6%
        Site3_1_1
        25.0%
        50%
        0.0M
        Site3_1_2
        50.0%
        49%
        0.0M
        Site3_2
        13.4%
        Site3_2_1
        97.9%
        48%
        0.2M
        Site3_2_2
        97.3%
        46%
        0.2M
        Site3_3
        13.4%
        Site3_3_1
        94.7%
        48%
        0.1M
        Site3_3_2
        97.3%
        47%
        0.1M
        Site4_1
        14.0%
        Site4_1_1
        96.5%
        50%
        0.1M
        Site4_1_2
        96.0%
        50%
        0.1M
        Site4_2
        11.5%
        Site4_2_1
        94.8%
        49%
        0.0M
        Site4_2_2
        94.7%
        49%
        0.0M
        Site4_3
        13.0%
        Site4_3_1
        97.2%
        50%
        0.4M
        Site4_3_2
        97.6%
        49%
        0.4M
        XB
        54.4%
        XB_1
        95.9%
        63%
        0.0M
        XB_2
        96.2%
        62%
        0.0M

        Cutadapt

        Cutadapt is a tool to find and remove adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.DOI: 10.14806/ej.17.1.200.

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Trimmed Sequence Lengths (5')

        This plot shows the number of reads with certain lengths of adapter trimmed for the 5' end.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        FastQC

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Length Distribution

        All samples have sequences of a single length (251bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceSamplesOccurrences% of all reads
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        13
        8141
        0.1665%
        GTCGGTAAAACTCGTGCCAGCCACCGCGGTTATACGAGAGGCCCTAGTTG
        12
        274897
        5.6237%
        GTCGGTAAAACTCGTGCCAGCAGCAGCGGTAATACGAGTGCCCCAAGCGT
        12
        16979
        0.3473%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAAGACGGAGGATGCAAGTGT
        11
        892519
        18.2587%
        GTCGGTAAAACTCGTGCCAGCCACCGCGGTTATACGAGAGGCCCAAGTTG
        11
        148212
        3.0321%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAAGACGAGCTCCCCAAACGT
        11
        33199
        0.6792%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACGAAGGTCCCGAGCGT
        11
        30780
        0.6297%
        GTCGGTAAAACTCGTGCCAGCCACCGCGGTTAGACGAGAGGCCCAAGTTG
        11
        113631
        2.3246%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACGAAGGTCCCAAGCGT
        11
        16093
        0.3292%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGT
        11
        12013
        0.2458%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAAGACGAGCTCTCCAAACGT
        11
        15161
        0.3102%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACAGAGGTCTCAAGCGT
        11
        12411
        0.2539%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAAGACGAACCGTGCGAACGT
        11
        20114
        0.4115%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACGAGAGGCCCAAACGT
        11
        15085
        0.3086%
        GTCGGTAAAACTCGTGCCAGCAGCAGCGGTAATACGAGTGCCCCGAGCGT
        11
        21772
        0.4454%
        GTCGGTAAAACTCGTGCCAGCCACCGCGGTTATACGAGAGGCTCAAGTTG
        11
        55207
        1.1294%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAAGACGAACCGTCCAAACGT
        11
        14420
        0.2950%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACGAACTGTGCGAACGT
        11
        13656
        0.2794%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAATACGGGGGGGGCAAGCGT
        11
        10420
        0.2132%
        GTCGGTAAAACTCGTGCCAGCAGCCGCGGTAAGACGAGTCTCACGAACGT
        11
        12063
        0.2468%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        nf-core/ampliseq Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/ampliseq v2.11.0 (doi: 10.5281/zenodo.1493841), (doi: 10.3389/fmicb.2020.550420) (Straub et al., 2020) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.04.4 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/ampliseq -profile singularity --input /work/gmgi/Fisheries/eDNA/NY/metadata/samplesheet.csv --FW_primer GTCGGTAAAACTCGTGCCAGC --RV_primer CATAGTGGGGTATCTAATCCCAGTTTG --outdir /work/gmgi/Fisheries/eDNA/NY/results --trunc_qmin 25 --max_len 250 --max_ee 2 --sample_inference pseudo --skip_taxonomy --ignore_failed_filtering --ignore_failed_trimming

        References

        • Straub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S. Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline. Front Microbiol. 2020 Oct 23;11:550420. https://doi.org/10.3389/fmicb.2020.550420
        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/ampliseq Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        revision
        master
        runName
        focused_lumiere
        containerEngine
        singularity
        launchDir
        /work/gmgi/Fisheries/eDNA/NY/scripts
        workDir
        /work/gmgi/Fisheries/eDNA/NY/scripts/work
        projectDir
        /home/e.strand/.nextflow/assets/nf-core/ampliseq
        userName
        e.strand
        profile
        singularity
        configFiles
        N/A

        Main arguments

        input
        /work/gmgi/Fisheries/eDNA/NY/metadata/samplesheet.csv
        FW_primer
        GTCGGTAAAACTCGTGCCAGC
        RV_primer
        CATAGTGGGGTATCTAATCCCAGTTTG
        outdir
        /work/gmgi/Fisheries/eDNA/NY/results

        Primer removal

        ignore_failed_trimming
        true

        Read trimming and quality filtering

        max_len
        250
        ignore_failed_filtering
        true

        Amplicon Sequence Variants (ASV) calculation

        sample_inference
        pseudo

        Skipping specific steps

        skip_taxonomy
        true